Differential privacy (DP) [1]}, [2]}, [3]} has become the de-facto definition of algorithmic privacy.
An algorithm \(\mathcal {M}\)  is said to be \((\varepsilon ,\delta )\) -differentially private
if for all set of events \(S \subseteq \text{Range}(\mathcal {M})\) ,
for all neighboring data sets \(D,D^{\prime } \in \mathcal {D}^n\)  (where \(\mathcal {D}\)  is the set of all possible data points ) that differ in only one sample:
\(\Pr [\mathcal {M}(D) \in S ] \le e^{\varepsilon } \Pr [\mathcal {M}(D^{\prime }) \in S] + \delta .\) 
